A Multiscale Aligned Video Super-Resolution Network for Improving Vibration Signal Measurement Accuracy

The quality of sequence frames is the premise of obtaining accurate vibration curves by visual vibration measurement method. Existing methods for video super-resolution reconstruction (VSR) have large models and slow inference speeds, which are not suitable for practical industrial applications. This article proposes a video super-resolution network framework based on the pyramid and deformable convolution to measure the three key indicators of algorithm accuracy, speed, and model size. Specifically, we designed a pyramid alignment module (PAM) that includes a deformable self-attention module (DSA) to focus on the motion estimation of the region of interest while suppressing unrealistic motion estimation. In addition, the two-way propagation method combined with the PAM can be used to ensure that all frames in the input sequence can be balanced to enjoy the information of the full sequence of frames when aligned. The experimental results show that the proposed method improves the peak signal-to-noise ratio (PSNR) by 0.58 dB compared with the existing reconstruction algorithm on the high-speed rotor image dataset produced in this article and reduces the reasoning time by 41.5% and the model size by 9.1%. Finally, the rotor vibration displacement analysis experiment shows that the vibration signal obtained from the rotor data enhanced by this algorithm has better periodicity and stability. Our code will be available at
https://github.com/candygogogogo/Rotor-Sequence-Image-Enhancement
.